| Machine learning–accelerated computational fluid dynamics D Kochkov, JA Smith, A Alieva, Q Wang, MP Brenner, S Hoyer Proceedings of the National Academy of Sciences 118 (21), e2101784118, 2021 | 873 | 2021 |
| Learning memory access patterns M Hashemi, K Swersky, J Smith, G Ayers, H Litz, J Chang, C Kozyrakis, ... International Conference on Machine Learning, 1919-1928, 2018 | 253 | 2018 |
| TF-Agents: A library for reinforcement learning in tensorflow S Guadarrama, A Korattikara, O Ramirez, P Castro, E Holly, S Fishman, ... GitHub repository, 2018 | 185 | 2018 |
| Number-theoretic nature of communication in quantum spin systems C Godsil, S Kirkland, S Severini, J Smith Physical review letters 109 (5), 050502, 2012 | 133 | 2012 |
| Score-based diffusion models as principled priors for inverse imaging BT Feng, J Smith, M Rubinstein, H Chang, KL Bouman, WT Freeman Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 55 | 2023 |
| Neural general circulation models D Kochkov, J Yuval, I Langmore, P Norgaard, J Smith, G Mooers, J Lottes, ... arXiv preprint arXiv:2311.07222, 2023 | 43 | 2023 |
| Strongly cospectral vertices C Godsil, J Smith arXiv preprint arXiv:1709.07975, 2017 | 41 | 2017 |
| Learning to correct spectral methods for simulating turbulent flows G Dresdner, D Kochkov, P Norgaard, L Zepeda-Núñez, JA Smith, ... arXiv preprint arXiv:2207.00556, 2022 | 38 | 2022 |
| Estimating the spectral density of large implicit matrices RP Adams, J Pennington, MJ Johnson, J Smith, Y Ovadia, B Patton, ... arXiv preprint arXiv:1802.03451, 2018 | 34 | 2018 |
| Tensorflow estimators: Managing simplicity vs. flexibility in high-level machine learning frameworks HT Cheng, Z Haque, L Hong, M Ispir, C Mewald, I Polosukhin, ... Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017 | 33 | 2017 |
| Variational data assimilation with a learned inverse observation operator T Frerix, D Kochkov, J Smith, D Cremers, M Brenner, S Hoyer International Conference on Machine Learning, 3449-3458, 2021 | 30 | 2021 |
| TF-Agents: A library for reinforcement learning in tensorflow. 2018 S Guadarrama, A Korattikara, O Ramirez, P Castro, E Holly, S Fishman, ... URL https://github. com/tensorflow/agents, 2019 | 28 | 2019 |
| Neural general circulation models for weather and climate D Kochkov, J Yuval, I Langmore, P Norgaard, J Smith, G Mooers, ... Nature 632 (8027), 1060-1066, 2024 | 27 | 2024 |
| Algorithms for quantum computers J Smith, M Mosca arXiv preprint arXiv:1001.0767, 2010 | 27 | 2010 |
| Optimal control of nonequilibrium systems through automatic differentiation MC Engel, JA Smith, MP Brenner Physical Review X 13 (4), 041032, 2023 | 23 | 2023 |
| Deep learning for Bayesian optimization of scientific problems with high-dimensional structure S Kim, PY Lu, C Loh, J Smith, J Snoek, M Soljačić arXiv preprint arXiv:2104.11667, 2021 | 20 | 2021 |
| Scalable and flexible deep Bayesian optimization with auxiliary information for scientific problems S Kim, PY Lu, C Loh, J Smith, J Snoek, M Soljacic arXiv preprint arXiv:2104.11667 1 (2), 3, 2021 | 15 | 2021 |
| Critiquing protein family classification models using sufficient input subsets B Carter, M Bileschi, J Smith, T Sanderson, D Bryant, D Belanger, ... Journal of Computational Biology 27 (8), 1219-1231, 2020 | 12 | 2020 |
| Algebraic aspects of multi-particle quantum walks J Smith University of Waterloo, 2012 | 11 | 2012 |
| Ensembles of classifiers: a bias-variance perspective N Gupta, J Smith, B Adlam, ZE Mariet Transactions on Machine Learning Research, 2022 | 9 | 2022 |
Michael P. BrennerHarvard UniversityVerified email at seas.harvard.edu
